Insights
Insights
Frameworks, analysis, and perspectives on product analytics in the AI era.
Featured Framework
The Industrialization of Knowledge Work
A framework for understanding how AI changes organizations, not just productivity. Why the real advantage is building systems, not buying tools.
Read the full framework →The Minimum Viable Experiment: A Framework for Early-Stage Rigor
Early-stage companies either skip experimentation entirely or replicate enterprise complexity. The middle ground requires only three components, and the most important one is the part teams skip.
Read more →North Star Metrics for AI Products: Why DAU Is the Wrong Choice
Traditional SaaS north star metrics reward login frequency. AI products need a metric that captures repeated value extraction, not repeated visits.
Read more →Will AI Replace Data Analysts? No. But It Will Expose Which Ones Were Misallocated.
AI eliminates the wrong kind of analyst work. What remains is the highest-value work most teams have been underinvesting in for years.
Read more →The Case for a Fractional Head of Data
Most growing companies need a data strategy, not another analyst. A fractional data leader builds the system in 90 days, then hands off execution.
Read more →Your A/B Test Didn't Fail. You Measured the Wrong Metric.
Most experiments fail not because the hypothesis was wrong, but because the team picked a metric that couldn't capture what they actually cared about.
Read more →The Decision Memo Template Your Data Team Needs (And Isn't Using)
The most effective analytical output is not a dashboard or a slide deck. It is a one-page decision memo with four sections.
Read more →Why Most AI Startups Get Their Activation Metric Wrong
The activation metric that worked for SaaS doesn't translate to AI products. Here's how to find the one that actually predicts retention.
Read more →Why Your Data Team's Best Analysis Never Drives Action
Every analytics team has a graveyard of Jupyter notebooks containing genuine insights nobody acted on. The gap between analysis and action is not technical. It is communicative.
Read more →Your AI Product's Retention Curve Is Lying to You
Blended retention curves hide the behavioral segments that matter most. Here's how to decompose them into actionable intelligence.
Read more →Why Dashboards Fail to Drive Decisions (And What to Build Instead)
A dashboard tells you what happened. A decision system tells you what to do about it. Most organizations are stuck on the wrong side of that line.
Read more →Data-Driven Is a Trap: Why the Most Popular Goal in Analytics Gets It Wrong
Data-driven sounds rigorous. It is also the wrong goal. The companies that make better decisions aim for something subtly but critically different.
Read more →Analytics Maturity Assessment: Every Company Overestimates. Here's Where You Actually Sit.
Most companies that call themselves data-driven sit at Level 2 of a five-level maturity framework. The gap to Level 4 is not technical. It is organizational.
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